pocket-ai -- A Portable Toolkit for building AI Infra.
https://github.com/cjmcv/pocket-ai
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engine/cl: A small computing framework based on opencl. This framework is designed to help you quickly call Opencl API to do the calculations you need.
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engine/vk: A small computing framework based on vulkan. This framework is designed to help you quickly call vulkan's computing API to do the calculations you need.
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engine/graph: A small multitasking scheduler that can quickly build efficient pipelines for your multiple tasks.
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engine/infer: A tiny inference engine for microprocessors, with a library size of only 10K+.
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eval/llm: A small tool is used to quickly verify whether the end-to-end calculation results are correct when accelerating and optimizing the large language model (LLM) inference engine.
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Other small tools.
cux -- An experimental framework for performance analysis and optimization of CUDA kernel functions.
https://github.com/cjmcv/hpc/tree/master/0-frameworks/cux
tag: cuda / simd / openmp.
mrpc -- Mini-RPC, based on asio.
https://github.com/cjmcv/hpc/tree/master/0-frameworks/mrpc
tag: distributed computing.
DEPRECATED
hcs A heterogeneous computing system for multi-task scheduling optimization.
vky A Vulkan-based computing framework
"hcs" and "vky" have been moved to pocket-ai and renamed as graph and vk respectively.
cuda
- base_graph : Record the basic usage of cuda graph.
- base_unified_memory : A simple task consumer using threads and streams with all data in Unified Memory.
- base_zero_copy : Record the basic usage of Zero Copy.
- gemm_fp16_wmma : Gemm fp16 - wmma
- gemm_fp32 : Gemm fp32 - cuda core
vulkan
- gemm_fp32 : Gemm fp32.
opencl
- basic_demo : Introduce the basic calling method and process of OpenCL API (without using pocket-ai).
- gemm_f32 : Gemm fp32 for Discrete graphics card.
- gemm_mobile_f32 : Gemm fp32 for integrated graphics card.
neon
- gemm_fp32 : Gemm fp32.
- gemm_int8 : Gemm int8.
- matrix_transpose : Matrix Transpose.
sse/avx
- matrix_multiply : Matrix Multiplication.
- matrix_transpose : Matrix Transpose.
- vector_dot_product : Vector dot product: result = SUM(A * B).
- vector_scan : Scan. Prefix Sum.
mpi/mpi4py
- alg_matrix_multiply : gemm: C = A * B.
- base_broadcast_scatter_gather : Record the basic usage of Bcast, Scatter, Gather and Allgather.
- base_group : Group communication.
- base_hello_world : Environment Management Routines.
- base_reduce_alltoall_scan : Record the basic usage of Reduce, Allreduce, Alltoall, Scan and Exscan.
- base_send_recv : Record the basic usage of MPI_Send/MPI_Recv and MPI_ISend/MPI_IRecv.
- base_type_contiguous : Send and receive custom types of data by using MPI_Type_contiguous.
- base_type_struct : Send and receive custom types of data by using MPI_Type_struct.
- util_bandwidth_test : Test bandwidth by point-to-point communications.
- py_base_broadcast_scatter_gather : Record the basic usage of Bcast, Scatter, Gather and Allgather.
- py_base_reduce_scan : Record the basic usage of Reduce and Scan.
- py_base_send_recv : Record the basic usage of Send and Recv.
std
- alg_quick_sort: Quick sort using std::thread.
- alg_vector_dot_product: Vector dot product: h_result = SUM(A * B). Record the basic usage of std::tread and std::sync.
- base_async: Record the basic usage of std::async.
- util_blocking_queue: Blocking queue. Mainly implemented by thread, queue and condition_variable.
- util_internal_thread: Internal Thread. Mainly implemented by std::thread.
- util_thread_pool: Thread Pool. Mainly implemented by thread, queue, future and condition_variable.
openmp
- alg_matrix_multiply : gemm: C = A * B.
- alg_pi_calculate : Calculate PI using parallel, for and reduction.
- base_flush : Records the basic usage of flush.
- base_mutex : Mutex operation in openmp, including critical, atomic, lock.
- base_parallel_for : Parallel and For.
- base_schedule : Records the basic usage of schedule.
- base_sections_single : Records the basic usage of Sections and Single.
- base_synchronous : Synchronous operation in openmp, including barrier, ordered and master.
tbb
- base_allocator : The basic use of allocator.
- base_atomic : The basic use of atomic.
- base_concurrent_hash_map : The basic use of concurrent_hash_map.
- base_concurrent_queue : The basic use of concurrent queue.
- base_mutex : The basic use of mutex in tbb.
- base_parallel_for : The basic use of parallel_for.
- base_parallel_reduce : The basic use of parallel_reduce.
- base_parallel_scan : The basic use of parallel_scan.
- base_parallel_sort : The basic use of base_parallel_sort.
- base_task_scheduler : The basic use of base_task_scheduler.
- count_strings : Count strings. Use the concurrent_hash_map.
libco
asyncio
- base_future: Record the basic usage of future.
- base_gather: Use gather to execute tasks in parallel.
- base_hello_world: Hello world. Record the basic usage of async, await and loop.
- base_loop_chain: Executes nested coroutines.